Article
Instruments & Instrumentation
Pengfei Zhang, Zhuopin Xu, Huimin Ma, Weimin Cheng, Xiaohong Li, Liwen Tang, Guangxia Zhao, Yuejin Wu, Zan Liu, Qi Wang
Summary: The dimensionality of spectral data is increasing, so there is a need for high-performance variable selection algorithms in chemometrics. This study proposes a novel MWO-BOSS method for variable selection based on the BOSS algorithm, with three improvement strategies. The MWO-BOSS algorithm effectively improves the predictive ability of the model and performs best among the tested datasets.
INFRARED PHYSICS & TECHNOLOGY
(2023)
Article
Chemistry, Analytical
Debora Gonsalves Carvalho, Lucas Ranzan, Rosangela Assis Jacques, Luciane Ferreira Trierweiler, Jorge Otavio Trierweiler
Summary: This study quantified total phenolic compounds and caffeine in green and black tea using fluorescence, MIR, and NIR spectroscopy combined with spectral variables selection tools. The results showed that these spectroscopic techniques, with the PSCM algorithm, could accurately predict total phenolic and caffeine content using a small number of variables.
MICROCHEMICAL JOURNAL
(2021)
Article
Spectroscopy
Mohamed B. El-Zeiny, Hossam M. Zawbaa, Ahmed Serag
Summary: This study introduces the grey wolf optimization (GWO) and antlion optimization (ALO) algorithms as variable selection tools in spectroscopic data analysis for the first time, showing that they select fewer variables than genetic algorithm (GA) and particle swarm optimization (PSO) algorithm in most cases while maintaining almost the same performance.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2021)
Article
Automation & Control Systems
Jie Shan, Jeng-Shyang Pan, Cheng-Kuo Chang, Shu-Chuan Chu, Shi-Guang Zheng
Summary: Firefly algorithm is a nature-inspired optimization algorithm that has been widely used due to its superior performance. This paper introduces a distributed parallel firefly algorithm with four communication strategies to improve its shortcomings, and experimental results show its competitiveness in CEC2013 test suite. The proposed algorithm is also successfully applied to PID parameter tuning of variable pitch wind turbine, outperforming other algorithms.
Article
Computer Science, Interdisciplinary Applications
Ahmed A. Ewees, Laith Abualigah, Dalia Yousri, Zakariya Yahya Algamal, Mohammed A. A. Al-qaness, Rehab Ali Ibrahim, Mohamed Abd Elaziz
Summary: Feature selection methods are essential for developing intelligent analysis tools that require data preprocessing and improving the performance of machine learning algorithms. This paper introduces a new feature selection method based on the modified Slime mould algorithm using the firefly algorithm. Experimental results confirm the promising performance of the method across different performance measures.
ENGINEERING WITH COMPUTERS
(2022)
Article
Food Science & Technology
Muhammad Hilal Kabir, Mahamed Lamine Guindo, Rongqin Chen, Xinmeng Luo, Wenwen Kong, Fei Liu
Summary: This study used laser-induced breakdown spectroscopy coupled with variable selection and chemometrics to quickly and accurately detect heavy metals (Cd, Cu, and Pb) in Fritillaria thunbergii by analyzing selected variables. The results showed that this method can improve detection efficiency and accuracy.
Article
Engineering, Biomedical
Weidong Xie, Linjie Wang, Kun Yu, Tengfei Shi, Wei Li
Summary: Gene microarray technology plays a crucial role in disease diagnosis. In this paper, an improved multilayer binary firefly-based method is proposed to reduce data dimensionality and optimize feature space. Experimental results show that the method achieves higher classification accuracy with fewer features.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Asma M. Altabeeb, Abdulqader M. Mohsen, Laith Abualigah, Abdullatif Ghallab
Summary: The study introduces a cooperative hybrid firefly algorithm to solve the capacitated vehicle routing problem (CVRP), which utilizes multiple firefly algorithm populations to collaborate, hybridizes with local search and genetic operators, and exchanges solutions among populations through communication, the results of experiments demonstrate the algorithm's outstanding performance compared to other methods.
APPLIED SOFT COMPUTING
(2021)
Review
Chemistry, Applied
Adriano de Araujo Gomes, Silvana M. Azcarate, Paulo Henrique Gonsalves Dias Diniz, David Douglas de Sousa Fernandes, Germano Veras
Summary: Food analysis is crucial for ensuring the quality and integrity of food products, and selecting effective variables is key to improving the accuracy and robustness of models. By discarding non-informative and redundant signals, more accurate and interpretable models can be established.
Article
Spectroscopy
Pengfei Zhang, Zhuopin Xu, Qi Wang, Shuang Fan, Weimin Cheng, Haiping Wang, Yuejin Wu
Summary: A new wavelength selection algorithm based on CMW and VDPSO algorithms is proposed in this study, with VDPSO-CMW showing better performance in experiments.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2021)
Article
Computer Science, Artificial Intelligence
Xin Yong, Yue-lin Gao
Summary: Feature selection is a popular approach in data mining tasks for improving algorithm performance and obtaining more information. This research proposes an improved firefly algorithm for feature selection with reliefF-based initialization and weighted voting mechanism. Experimental results demonstrate the superiority of the proposed algorithm.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Automation & Control Systems
Haoran Li, Jisheng Dai, Jianbo Xiao, Xiaobo Zou, Tao Chen, Melvin Holmose
Summary: In this study, the RAH algorithm is combined with LASSO to fit the entire solution of the LASSO problem by tracking KKT conditions and selecting the optimal regularization parameter. The results demonstrate that RAH-LASSO + PLS outperforms other methods in terms of wavelength selection and calibration.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2022)
Article
Agronomy
Maylin Acosta, Isabel Rodriguez-Carretero, Jose Blasco, Jose Miguel de Paz, Ana Quinones
Summary: Visible and near-infrared hyperspectral imaging was used to determine the nutrient contents in persimmon leaves. The models based on partial least square regression achieved satisfactory results for nitrogen, phosphorous, calcium, magnesium, and boron, but lower prediction rates were attained for potassium, iron, copper, zinc, and manganese.
Article
Chemistry, Analytical
Haoran Li, Pengcheng Wu, Jisheng Dai, Xiaobo Zou
Summary: In this study, we propose a novel multiple featurespaces ensemble strategy combined with the LASSO method to address the variable selection problem in spectroscopic calibration. Experimental results demonstrate significant improvements in both the consistency of variable selection and prediction performance compared to benchmark methods.
ANALYTICA CHIMICA ACTA
(2023)
Article
Computer Science, Theory & Methods
Heru Nugroho, Nugraha Priya Utama, Kridanto Surendro
Summary: This study aims to perform the imputation process using the smoothing target encoding (STE) method combined with C3FA and standard deviation (STD), and compare it with other imputation methods. The results showed that the proposed method (C3FA-STD) achieved AUC, CA, F1-Score, precision, and recall values of 0.939, 0.882, 0.881, 0.881, and 0.882, respectively, based on the evaluation using the kNN classifier.
JOURNAL OF BIG DATA
(2023)
Article
Engineering, Biomedical
Phornpot Chainok, Karla de Jesus, Leandro Coelho, Helon Vicente Hultmann Ayala, Mateus Gheorghe de Castro Ribeiro, Ricardo J. Fernandes, Joao Paulo Vilas-Boas
Summary: The purpose of this study was to predict the performance determinant factors of 15m backstroke-to-breaststroke turning using machine-learning models and comparing linear and tree-based models. The collected data revealed that the best models showed similar performance in different turning techniques, with balanced contributions between turn-in and turn-out variables.
SPORTS BIOMECHANICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Marcia de Fatima Morais, Matheus Henrique Dal Molin Ribeiro, Ramon Gomes da Silva, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: This paper proposes three optimization algorithms based on discrete differential evolution (DE) metaheuristics for permutation flow shop (PFS) scheduling problems. The performance of the algorithms is evaluated using various benchmarks, and the results show promising and competitive performance in terms of average performance values.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Review
Computer Science, Artificial Intelligence
Luiza Scapinello Aquino da Silva, Yan Lieven Souza Lucio, Leandro dos Santos Coelho, Viviana Cocco Mariani, Ravipudi Venkata Rao
Summary: The Jaya Algorithm, a population-based optimization method, has become a valuable tool in swarm intelligence. This paper provides a comprehensive review and bibliometric study of the algorithm's applicability and variants, emphasizing its versatility. The study aims to inspire new researchers to utilize this simple and efficient algorithm for problem-solving. Evaluation: 8/10.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Mathematics, Interdisciplinary Applications
Matheus Henrique Dal Molin Ribeiro, Ramon Gomes da Silva, Gabriel Trierweiler Ribeiro, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: Efficient models for short-term load forecasting in electricity distribution and generation systems are crucial for companies' energetic planning. In this study, an ensemble learning model based on dual decomposition approach, machine learning models and hyperparameters optimization is proposed. The model successfully decomposes the time series and handles the non-linearities, and achieves accurate load forecasting results with reduced errors.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Energy & Fuels
Stefano Frizzo Stefenon, Laio Oriel Seman, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: The cost of electricity and gas has a direct impact on people's everyday routines, but the value of electricity is closely related to spot market prices, which can increase in winter due to higher energy demand. Existing models for forecasting energy costs are not robust enough due to competition, seasonal changes, and other variables. This study proposes combining seasonal and trend decomposition using LOESS and Facebook Prophet methodologies to improve the accuracy of analyzing time series data on Italian electricity spot prices.
Article
Computer Science, Artificial Intelligence
Allan Christian Krainski Ferrari, Gideon Villar Leandro, Leandro dos Santos Coelho, Myriam Regattieri De Biase Silva Delgado
Summary: This work proposes a fuzzy mechanism to improve the convergence of the rat swarm optimizer algorithm. The proposed fuzzy model uses the normalized fitness and population diversity as input. The results show that the fuzzy mechanism improves convergence and is competitive with other metaheuristics.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2023)
Article
Energy & Fuels
Anne Carolina Rodrigues Klaar, Stefano Frizzo Stefenon, Laio Oriel Seman, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: The energy price has a significant impact on investment and economic development. Forecasting future energy prices can support industrial planning and help avoid economic recession.
Article
Chemistry, Analytical
Anne Carolina Rodrigues Klaar, Stefano Frizzo Stefenon, Laio Oriel Seman, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: Insulators installed outdoors are prone to accumulation of contaminants, causing increased conductivity and leakage current, eventually leading to flashover. To enhance power system reliability, it is possible to predict fault development and potential shutdown by evaluating the increase in leakage current. This paper proposes a method, optimized EWT-Seq2Seq-LSTM with attention, which combines empirical wavelet transform (EWT) to reduce non-representative variations and the attention mechanism with LSTM recurrent network for prediction. The model achieved a 10.17% lower mean square error (MSE) compared to standard LSTM and a 5.36% lower MSE compared to the model without optimization, demonstrating the effectiveness of the attention mechanism and hyperparameter optimization.
Article
Chemistry, Analytical
Andressa Borre, Laio Oriel Seman, Eduardo Camponogara, Stefano Frizzo Stefenon, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: In this paper, the issue of predicting electrical machine failures by predicting possible anomalies in the data is addressed through time series analysis. The dataset is used to train a hybrid CNN-LSTM architecture, which employs quantile regression to manage uncertainties in the data. The results show that this approach outperforms traditional reference models, making it beneficial for companies to optimize maintenance schedules and improve the performance of their electric machines.
Article
Thermodynamics
Stefano Frizzo Stefenon, Laio Oriel Seman, Luiza Scapinello Aquino, Leandro dos Santos Coelho
Summary: This paper proposes a Seq2Seq LSTM neural network model with an attention mechanism and wavelet transform for reservoir level prediction. The proposed approach outperforms other models and provides accurate and timely predictions of water levels, allowing for better decision-making in dam management under emergency conditions.
Article
Computer Science, Artificial Intelligence
Alan Naoto Tabata, Alessandro Zimmer, Leandro dos Santos Coelho, Viviana Cocco Mariani
Summary: This study used synthetic datasets from the CARLA simulator and real-world dataset from WAYMO Open to train and evaluate computer vision algorithms. An efficient automated method for pedestrian and vehicle identification and counting was developed, which can quickly identify target features among many images and output formatted results.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Laio Oriel Seman, Stefano Frizzo Stefenon, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: This paper evaluates a time series of leakage current from a high-voltage laboratory experiment using porcelain pin-type insulators. Time series forecasting is performed with ensemble learning approaches, and the results show that applying these approaches enhances the performance of the machine learning models in predicting breakdowns in the electrical power system.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2023)
Article
Chemistry, Analytical
Guilherme Augusto Silva Surek, Laio Oriel Seman, Stefano Frizzo Stefenon, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: This paper aims to evaluate and map the current scenario of human actions in red, green, and blue videos using deep learning models. A semi-supervised learning approach is employed to evaluate a residual network (ResNet) and a vision transformer architecture (ViT). The results obtained using a bi-dimensional ViT structure demonstrated great performance in human action recognition, achieving an accuracy of 96.7% on the HMDB51 dataset.
Article
Chemistry, Analytical
Matheus Henrique Dal Molin Ribeiro, Ramon Gomes da Silva, Jose Henrique Kleinubing Larcher, Andre Mendes, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: This paper proposes a new hybrid framework combining STACK ensemble learning and a JADE algorithm for nonlinear system identification. The model performs well in decoding EEG signals, achieving an average explanation of 94.50% and 67.50% of data variability, and outperforms other methods in terms of accuracy.
Article
Chemistry, Analytical
Stefano Frizzo Stefenon, Laio Oriel Seman, Nemesio Fava Sopelsa Neto, Luiz Henrique Meyer, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: This paper presents a novel hybrid method for fault prediction based on the time series of leakage current of contaminated insulators. The proposed CFRW-GMDH method, with a root-mean-squared error of 3.44x10(-12), outperformed other models in fault prediction. This approach can provide power utilities with a reliable tool for monitoring insulator health and predicting failures, thereby enhancing the reliability of the power supply.
Article
Chemistry, Analytical
Yujia Ying, Huilin Li
Summary: Enzymes play a crucial role as biological catalysts in accelerating biochemical reactions in living organisms. This study proposes a convenient method for monitoring enzymatic catalytic processes using native top-down mass spectrometry. By exploring the heterogeneity of the chymotrypsin sample and using tandem mass spectrometry, the researchers were able to monitor covalent and noncovalent enzymatic complexes, substrates, and products during catalysis. The results demonstrate that this method has the potential to be a promising tool for characterizing biocatalysts.
ANALYTICA CHIMICA ACTA
(2024)
Article
Chemistry, Analytical
Kang Yang, Shuaibo Shi, Jinyu Wu, Shaolong Han, Shengdi Tai, Shishen Zhang, Kun Zhang
Summary: H2O and D2O are important analogues closely related to various industries and monitoring fields. This study successfully distinguishes and detects H2O and D2O by designing a novel eu(III)-macrocycle with dual emitters, Eu-2a, and conducting fluorescence titration experiments.
ANALYTICA CHIMICA ACTA
(2024)
Article
Chemistry, Analytical
Julia Kuligowski, Alvaro Perez-Rubio, Marta Moreno-Torres, Polina Soluyanova, Judith Perez-Rojas, Ivan Rienda, David Perez-Guaita, Eugenia Pareja, Ramon Trullenque-Juan, Jose Castell, Marcha Verheijen, Florian Caiment, Ramiro Jover, Guillermo Quintas
Summary: This study introduces a novel variable selection approach called cluster PLS (c-PLS) to assess the joint impact of variable groups selected based on biological characteristics on the predictive performance of a multivariate model. The usefulness of c-PLS is shown using miRNomic and metabolomic datasets obtained from the analysis of liver tissue biopsies.
ANALYTICA CHIMICA ACTA
(2024)
Article
Chemistry, Analytical
Meixue Lai, Lijie Zhong, Siyi Liu, Yitian Tang, Tingting Han, Huali Deng, Yu Bao, Yingming Ma, Wei Wang, Li Niu, Shiyu Gan
Summary: This study presents a method for constructing wearable sweat electrolyte sensors using carbon fiber-based solid-contact ion-selective electrodes (SC-ISEs). By using carbon fibers extracted from commercial cloth as electrode material, the cost and reproducibility issues of flexible SC-ISEs were addressed. The results showed that the carbon fiber-based SC-ISEs exhibited reversible voltammetric and stable impedance performances, and had high reproducibility of standard potentials between normal and bending states.
ANALYTICA CHIMICA ACTA
(2024)
Article
Chemistry, Analytical
Ke Yang, Ying Liu, Min Deng, Peipei Wang, Dan Cheng, Songjiao Li, Longwei He
Summary: A near-infrared fluorescent probe has been developed to accurately measure the levels of peroxynitrite (ONOO-) in the endoplasmic reticulum (ER) in acute lung injury (ALI). The probe demonstrated rapid response, high selectivity, good sensitivity, and enhanced fluorescence intensity in response to ONOO-. It was successfully used to detect changes in ONOO- levels and showed significant increases in an ALI cell model and an ALI mouse model.
ANALYTICA CHIMICA ACTA
(2024)
Article
Chemistry, Analytical
Yanping Wang, Yuemei Chen, Kejun Li, Jinrong Zhou, Xin Yuan, Mei Zhang, Ke Huang
Summary: In this work, a portable analytical system based on point discharge chemical vapor generation atomic emission spectrometry (PD-CVG-AES) coupling with gold filament enrichment was designed. The highly sensitive analysis of Hg2+ indirectly realized the detection of ascorbic acid (AA). The measurement is based on the fact that Ag+ can decrease the concentration of Hg2+ by forming Ag-Hg amalgam in the presence of the reductant SnCl2, while AA can pre-reduce Ag+ to Ag0, leading to the generation of silver nanoparticles (Ag NPs). The developed novel analytical strategy broadens the application of microplasma-based AES and offers a higher level of sensitivity compared to current AA detection techniques.
ANALYTICA CHIMICA ACTA
(2024)
Article
Chemistry, Analytical
Yundi Huang, Bo Song, Kaiwen Chen, Deshu Kong, Jingli Yuan
Summary: This study developed two lysosome-targetable background-free TGL probes for efficient and accurate detection of 1O2, which can be used for monitoring endogenous 1O2 concentrations in lysosomes and discriminating variability induced by different photosensitizers. Furthermore, a smart luminescent sensor film was successfully prepared for on-site 1O2 production detection during PDT processes, offering a promising clinical monitoring tool for skin diseases.
ANALYTICA CHIMICA ACTA
(2024)
Article
Chemistry, Analytical
Wei Lang, Jia-Mei Qin, Qian-Yong Cao
Summary: A novel polymer-based probe P1 was successfully synthesized for fluorescently ratiometric sensing of H2S with high selectivity and sensitivity. A smartphone sensing platform was constructed to conduct visual quantitative detection of H2S. P1 can be employed in evaluating the level fluctuations of H2S in living cells, testing water samples/wine samples, and monitoring food freshness.
ANALYTICA CHIMICA ACTA
(2024)
Article
Chemistry, Analytical
Yuanyuan Qin, Shuda Liu, Shuyun Meng, Dong Liu, Tianyan You
Summary: The study developed a split aptamer-based sandwich-type ratiometric biosensor for the detection of 17 beta-estradiol (E2) using photoelectrochemical and electrochemical assays. The biosensor utilized split aptamer fragments to recognize E2 and trigger a hybridization chain reaction, resulting in the production of double-stranded DNA labeled with CdTe quantum dots. This DNA complex was able to sensitize CdTe quantum dots and generate response signals for E2 detection. The developed biosensor demonstrated high sensitivity and accuracy with two linear ranges and low detection limits.
ANALYTICA CHIMICA ACTA
(2024)
Article
Chemistry, Analytical
Siriwan Teepoo, Jongjit Jantra, Khaunnapa Panapong, David Taiwo Ajayi
Summary: A novel immunochromatographic assay based on hyperbranched Au plasmonic blackbodies with a smartphone readout was developed for rapid and sensitive detection of leucomalachite green residues in fish and shrimp products.
ANALYTICA CHIMICA ACTA
(2024)
Article
Chemistry, Analytical
Andrea L. Larraga-Urdaz, Borja Moreira-Alvarez, Jorge Ruiz Encinar, Jose M. Costa-Fernandez, Maria Luisa Fernandez-Sanchez
Summary: A major challenge in the 21st century is the development of point-of-care diagnostic tools. In this study, a highly sensitive and simple bioassay using AuNPs and MNAzymes was developed for rapid detection and quantification of miRNA-4739. The proposed strategy shows potential for breast cancer diagnosis.
ANALYTICA CHIMICA ACTA
(2024)
Article
Chemistry, Analytical
Xiaohan Zhao, Anyu Wang, Lingzi Zhai, Jiuhe Gao, Sizhe Lyu, Yingshan Jiang, Tian Zhong, Ying Xiao, Xi Yu
Summary: A novel method using polystyrene-coated magnetic nanoparticles for extracting monohydroxy polycyclic aromatic hydrocarbons from urine samples was investigated. The proposed method is simple, sensitive, and efficient, with desirable sensitivity for analyzing low-abundance metabolites in large volumes of complex urine samples.
ANALYTICA CHIMICA ACTA
(2024)
Article
Chemistry, Analytical
Ke Quan, Yuqing Zeng, Wenke Zhang, Fengfeng Li, Mengjiao Li, Zhihe Qing, Linlin Wu
Summary: In this study, a visual and reusable biosensor was developed for detecting substrates that are closely associated with human physiological health. The immobilized oxidase showed higher stability and sensitivity under harsh conditions, enabling reliable detection of substrates in complex fluids.
ANALYTICA CHIMICA ACTA
(2024)
Article
Chemistry, Analytical
Dongjuan Wang, Xiuqian Ding, Jinling Xie, Juan Wang, Guanhao Li, Xin Zhou
Summary: In this study, a three-in-one sensor was developed for real-time detection of biogenic amines (BAs) with high sensitivity and selectivity. The sensor showed multimodal responses and could be used to fabricate portable devices for on-site non-destructive assessment of food spoilage indicators.
ANALYTICA CHIMICA ACTA
(2024)
Article
Chemistry, Analytical
Jinting Meng, Zihao Xu, Shasha Zheng, Hongqun Yang, Tianfu Wang, Hong Wang, Yingwei Zhang
Summary: This study demonstrates the development of a cascade signal amplification system using a multi-pedal DNA walker and strand displacement reactions for the electrochemical detection of miRNA-155. The biosensor exhibited high sensitivity, selectivity, and the potential for clinical applications.
ANALYTICA CHIMICA ACTA
(2024)